Machine learning in understanding environmental variability of vibriosis in coastal waters.

comprise ecologically significant bacteria that thrive in warm, moderately saline water, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, . infections have been reported more frequently and over a greater geographical area along the US eastern seaboard. This study provides an analysis of . infections, notably caused by , non-O1/non-O139, , , , and , extracted from the Centers for Disease Control and Prevention's Cholera and Other Illness Surveillance system, located within 200 km of the eastern US coast, to analyze latitudinal distribution trends between 1990 and 2019. For each ., case incidence (presence/absence) was modeled, including environmental data and employing extreme gradient boosting machine learning algorithms (XGBoost). Environmental parameters associated with the incidence of vibriosis were clustered using k-means clustering. The northern limit of total cases of vibriosis was found to have increased . 40 km/year, with (. 70 km/year), (. 60 km/year), and (. 60 km/year) showing the greatest latitudinal shifts. These changes were found to be linked to environmental parameters that enhance the proliferation of . The average accuracy of the XGBoost models was 60.9%-71.0%, with temperature and salinity being the most significant predictors. Relationships among other environmental parameters were complex and nonlinear, but phytoplankton and precipitation served to differentiate the models. Clustering using k-means yielded results that supported temperature, salinity, and phytoplankton as important environmental parameters. Research in progress will aid in developing global predictive risk models for . infections.IMPORTANCE. are ecologically significant bacteria, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, . infections have been reported more frequently and over a greater geographical area along the US eastern seaboard. This study provides an analysis of latitudinal distribution trends of . infections, notably caused by , non-O1/non-O139, , , , and , within 200 km of the eastern US coast. The northern limit of total cases of vibriosis was found to have increased . 40 km/year. These changes were found to be linked to environmental parameters that enhance the proliferation of . Temperature and salinity were the most significant predictors of vibriosis case presence and absence. Phytoplankton and precipitation changes served to differentiate . presence. Research in progress will aid in developing global predictive risk models for . infections.